Development of a sensor data based descriptive and prescriptive system involves machine learning tasks like classification and regression. Any such system development requires the involvement of different stake-holders for understanding the problem domain and generate models for causality analysis, signal processing (SP), machine Learning (ML) techniques to perform data analysis and finally a developer to deploy solution. Now, the problem of developing such a system is that each of the stake holders speaks their own language and terms. In a related survey, it was found that the most difficult task in the above work-flow are, namely feature engineering (a combination of feature listing/extraction and feature selection), and in deep learning approaches such features are not interpretable for 1-D sensor signals and thus are prone to errors when it comes to performing prognostics and activity monitoring.